Go AI Reinforcement Learning Project - This repository is dedicated to exploring and comparing two reinforcement learning methods—gradient descent and Q-value learning—in developing intelligent agents for the board game Go. The goal is to observe the model’s evolution after generating thousands of self-played games and compare agents’ results.

Fake News Prediction Model

This project implements **Random Forest Regression** to predict the salary of an employee based on their position level. Using a dataset that includes position levels and corresponding salaries, this project demonstrates how an ensemble method like Random Forest can improve prediction accuracy by averaging multiple decision trees.

Explore advanced neural networks for crafting captivating headlines! Compare LSTM 🔄 and Transformer 🔀 models through interactive notebooks 📓 and easy-to-use wrapper classes 🛠️. Ideal for content creators and data enthusiasts aiming to automate and enhance headline generation ✨.

This project aims to train a predictive model to diagnose diabetes on women patients.

A Python implementation of multiple linear regression to predict the profit of startups based on their spending in R&D, Administration, Marketing, and the state they operate in.

Gene expression analysis and classification using XGBoost and Differential Expression Analysis (DES)

This repository contains a Loan Approval Prediction Model. The model predicts the likelihood of loan approval based on applicant data. The model deployment is done using FastAPI to allow applicant data to be entered in order to obtain an approval prediction.

I deployed this bi-disease prediction model in python using Machine Laerning. Deployed this ML model as a web application on cloud streamlit. To see the model please visit

Successfully established a machine learning model using PySpark which can precisely predict the energy consumption of the steel industry, up to an r2 score of approximately 99.5%.

a library so simple you will learn Within An Hour

Successfully established a machine learning model using PySpark which can accurately classify whether a bank customer will churn or not up to an accuracy of more than 86% on the test set.

ISeeYou is a model designed for binary image classification using the Boat-MNIST dataset. The dataset provides a simple hands-on benchmark to test small neural networks on the task of distinguishing between images containing watercraft and other images.

This repository includes a report about implementing a Linear Regression model to predict house prices using square footage, number of bedrooms, and number of bathrooms. The model demonstrated reliable performance and successfully predicted house prices for new input data.

The Pneumonia Detection App is a web application designed to assist in the diagnosis of pneumonia using chest X-ray images. This project utilizes deep learning techniques implemented with TensorFlow and Keras for image classification, and is deployed using Streamlit for a user-friendly interface.

Taxi Fare predictor using Random Forest and GridSearchCV for Hyperparameter Tuning

Different types of regression algorithm

Multi-class classification model to predict outcomes of cirrhosis patients using machine learning

This repository contains a machine learning project aimed at predicting housing prices in Boston. This project showcases the end-to-end process of building and deploying a machine learning model, from data preprocessing and model training to serialization and deployment.

Successfully fine-tuned a pretrained DistilBERT transformer model that can classify social media text data into one of 4 cyberbullying labels i.e. ethnicity/race, gender/sexual, religion and not cyberbullying with a remarkable accuracy of 99%.